Value driven integrated build-to-buy decision analysis system and method

ABSTRACT

A value-driven build-to-buy decision analysis system and method is provided that addresses and integrates the system analysis for multiple parts of the build-to-buy business cycle. The system and method of the present invention includes a demand component and a supply component. The demand component includes decision analysis components for one or more demand oriented activities in the build-to-buy cycle, such as selling and buying activities. The supply component includes decision analysis components for one or more supply oriented activities in the build-to-buy cycle, such as product creation, product packaging and product pricing activities. Each decision analysis component utilizes values-based object-oriented analytical models.

FIELD OF THE INVENTION

This invention relates to decision analysis systems and methods and,more specifically, to a value driven, integrated, build-to-buy decisionanalysis system and method.

BACKGROUND OF THE INVENTION

The Internet has resulted in unprecedented access to information. In thee-Commerce area, buyers of products and services are presented with amyriad of choices and are in need of decision analysis tools to helpthem manage the vast amount of information presented to them. Forbuilders of products and services, there is a need for decision analysistools that will help them make decisions related to creating andrefining product and service offerings. For sellers of products andservices, there is a need for decision analysis tools that will helpthem maximize company profits while increasing customer value.

Current providers of decision support tools generally target one of theactivities in the build-to-buy business cycle. Specifically, currentproviders generally target one of the following: self-service buyingactivities; development of products (e.g., determining what product tobuild or service to offer, and selecting pricing, promotions, packaging,warranties, service levels, features, etc.); or selling activities.Providers that target buying activities include Active Research™ (wwwdot activeresearch dot com), OnLink™ (www dot onlink dot com), AskJeeves™ (www dot askjeeves dot com), and Blaze Software (www dotblazesoft dot com). These providers typically use recommendation enginesthat are either not linked to a purchaser's input, or that forcepurchasers to think of only particular product features.

Providers that target development of products include PeopleSoft® (wwwdot peoplesoft dot com), Oracle® (www.dot oracle dot com), SAP™ (www dotsap dot com), HNC Software, Inc. (www dot hnc dot com), Hyperion® (wwwdot hyperion dot com) , Blox.com™ (www dot blox dot com), and CorVu (wwwdot corvu dot com). These providers generally offer general-purposebusiness performance management products that measure return oninvestment (ROI) and that track organizational performance for corporateinvestments or activities. These products typically take a long time toimplement and modify, and use inferred customer expectations.

Providers that target selling activities generally use rules-basedengines that utilize a fixed set of outcomes, and that take a long timeto implement and modify.

SUMMARY OF THE INVENTION

In view of the above problems in the art, the present invention providesa value-driven build-to-buy decision analysis system and method thataddresses and integrates decisions for multiple parts of thebuild-to-buy business cycle.

The system and method of the present invention includes a demandcomponent and a supply component. The demand component includes decisionanalysis components for one or more demand oriented activities in thebuild-to-buy cycle, such as selling and buying activities. The supplycomponent includes decision analysis components for one or more supplyoriented activities in the build-to-buy cycle (hereinafter “buildingactivities”), such as determining what products to build or services tooffer, and selecting pricing, promotions, packaging, warranties, servicelevels, and features (hereinafter collectively referred to as“products”).

Each decision analysis component utilizes object-oriented analyticalmodels. Further, the object-oriented analytical models are values-based,meaning that they utilize information about customer values in thedecision analysis. By using object-oriented analytical models, eachdecision analysis component can share information it gathers with othercomponents and utilize information it receives from other components.For example, information gathered during buying activities can bedirectly used during building activities, and information gatheredduring buying and building activities can be directly used to optimizeselling activities.

Object-oriented analytical models are preferably used that allow complexdecisions to be structured in a pictorial manner that can be executed insoftware. Unlike rules-based engines, the analytical model based systemand method of the present invention can show users the reasoning behindrecommendations and the implications of changes.

Using the system and method of the present invention, decision analysiscomponents can be created for a particular class of problems, and can beeasily reused across multiple customers in multiple industries. This isbecause, by using object-oriented analytical models, genericbuild-to-buy functionality is separated from elements unique to specificindustries and customers. This allows high degrees of customizationwithout long and costly implementation efforts.

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate the embodiments of the inventionand, together with the description, serve to explain the principles ofthe invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The preferred embodiments of this invention will be described in detail,with reference to the following figures, wherein:

FIG. 1 is a block diagram of a value-driven build-to-buy decisionanalysis system, in accordance with the present invention;

FIG. 2 is a schematic diagram of an object-oriented analytical modelused for the buy component of FIG. 1;

FIG. 3A is a diagram showing an example of steps that may be taken bythe buy component in assisting a customer with “self-service buying” ofcellular service over the internet;

FIG. 3B is a table, produced by the buy component of FIG. 2, listingtangible and intangible customer costs for the cellular service exampleshown in FIG. 3A;

FIG. 3C is a components of value chart, produced by the buy component ofFIG. 2, for the cellular service example shown in FIG. 3A;

FIG. 3D is a tornado chart, produced by the buy component of FIG. 2, forthe cellular service example shown in FIG. 3A;

FIG. 3E is a risk profile, produced by the build component of FIG. 2,for the cellular service example shown in FIG. 3A;

FIG. 4 is a schematic diagram of an object-oriented analytical modelused for the build component of FIG. 1;

FIG. 5A is a diagram showing an example of steps that may be taken bythe build component in assisting companies in a “build decision” forcellular service;

FIG. 5B is a components of value chart, produced by the build componentof FIG. 4, for the cellular service example shown in FIG. 5A;

FIG. 5C is a tornado chart, produced by the build component of FIG. 4,for the cellular service example shown in FIG. 5A;

FIG. 5D is a risk profile, produced by the build component of FIG. 4,for the cellular service example shown in FIG. 5A;

FIG. 6 is a schematic diagram of an object-oriented analytical modelused for the sell component of FIG. 1;

FIG. 7 is a diagram showing an example of steps that may be taken by thesell component in assisting company service representatives andautomated online service centers offer products to customers; and

FIG. 8 is a schematic diagram representation of the flow of informationin an integrated build-to-buy decision analysis method, in accordancewith the present invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

FIG. 1 is a block diagram of a value-driven build-to-buy decisionanalysis system 100. The system 100 includes a demand component 110 andsupply component 120. The demand component 110 includes decisionanalysis components for one or more demand oriented activities in thebuild-to-buy cycle. In the example shown in FIG. 1, the demand component110 includes a buy component 130 and a sell component 140. However, ifthe system 100 is designed for a self-service buying application, thesell component 140 may not be needed, as will be explained in moredetail below. The buy component 130 is designed to help customers makebuying decisions. The sell component 140 is designed to help CustomerService Representatives (CSRs) and online automated service centersoffer products and/or promotions to customers that will maximizecustomer value and company value.

In the example shown in FIG. 1 the supply component 120 includes a buildcomponent 150. The build component 150 is designed to help companieswith building activities, such as determining what products to build andoffer.

The decision analysis components 130, 140 and 150 utilizeobject-oriented analytical models. Further, the object-orientedanalytical models are values-based models that utilize information aboutcustomer values in the decision analysis. A customer's values definewhat is important to the customer in making a decision. For example, aperson may have many objectives in their life, such as spending timewith their family, going on a nice vacation, or having a successfulcareer. The associated objects of value for this person may includethings such as money, time, and prestige. A person's set of objectives,along with their relative importance, make up his or her “values.”

Companies have corporate objectives, such as increasing shareholdervalue, improving the lives of their employees, and contributing to thecommunity. The associated objects of value may include things such asmoney, employee satisfaction, and community growth. A company's set ofobjectives, along with their relative importance, make up the corporate“values”. In making decisions, customers need to make value tradeoffsbased on the relative importance of the various objects they value.

For example, if a customer is in the market for a computer, they willhave a vast number of choices available to him or her. The customer willdecide which computer to buy based, in part, on his or her own set ofvalues. For example, a customer may be willing to pay a higher price fora computer that has a fast processor because it will save the customertime. Alternatively, another customer may be willing to spend the extratime while working on the computer for a lower price. Yet anothercustomer may place significant value on the computer's appearance, andmay be willing to pay a higher price for an aesthetically pleasingcomputer. The object-oriented analytical models that are used for thedecision analysis components 130, 140 and 150 utilize this type of“value” information in the decision analysis.

Because the analytical models used are object-oriented, each decisionanalysis component can share information it gathers with othercomponents and utilize information it receives from other components.For example, information gathered during buying activities by the buycomponent 130 can be directly used by the build component 150 tooptimize building activities, and information gathered during buildingactivities by the build component 150 can be directly used by the sellcomponent 140, to optimize selling activities.

The object-oriented-analytical models allow complex decisions to bestructured in a pictorial manner that can be executed usingobject-oriented programming language, such as Java™ or C⁺⁺. Unlikerules-based engines, the analytical model based system and method of thepresent invention can show users the reasoning behind recommendationsand the implications of changes.

Using object-oriented analytical models, decision analysis components130, 140 and 150 can be created for a particular class of problems andcan be easily reused across multiple customers in multiple industries.This is because, by using object-oriented analytical models, genericbuild-to-buy functionality is separated from elements unique to specificindustries and customers. This allows high degrees of customizationwithout long and costly implementation efforts. Further, utilizing thesame object-oriented analytical models across multiple activities of thebuild-to-buy cycle dramatically improves the overall cycle.

The object-oriented analytical models incorporate Decision Analysistechniques, such as those described in Decision Analysis is described inTools for Making Acute Risk Decisions, Chapter 11 (Center for ChemicalProcess Safety of the American Institute of Chemical Engineers, 1995),and in Ralph L. Keeney, “Decision Analysis: An Overview,” OperationsResearch, Vol. 30, No. 5, September–October 1982. Decision analysis hashistorically been used for addressing individual decisions (e.g., shouldwe launch a product?; should we build a plant?; what method of removingwaste from a contaminated site should we use?; should we spend money onan R&D activity?; etc.). It has not previously been implemented in theseries of interrelated decisions in the build-to-buy cycle. The systemand method of the present invention exhibits the following features:

-   -   (1) Information is gathered and used in a probabilistic way. The        uncertainty in information is represented by probability        distributions. The distribution may be represented in its        mathematical form, by a discrete number of points that represent        the distribution, or presented as a single point that represents        the entire distribution. The method employed to represent the        probability distribution depends on the sophistication of the        user and the need for the probabilistic representation;    -   (2) Decision models are values-based. Value tradeoffs of        decision makers are assessed and presented in ways to help        decision makers factor them into decisions;    -   (3) Values gathered while assisting with the buy and sell        decisions are used in assessing build decisions; and    -   (4) Analytical results are presented to the decision maker in        ways that allow the decision maker to compare alternatives along        dimensions of value. Outputs may include, but are not limited        to, expected values, tornado diagrams, risk profiles, and        sensitivity diagrams.

FIG. 2 is a schematic diagram of an object-oriented analytical modelused for the buy component 130 shown FIG. 1. As discussed above, the buycomponent 130 is used to help customers make buying decisions, such ashelping the customer choose which product to buy among various competingproducts 160.

The model determines a customer value proposition 170 based on customerinformation 150, customer values 180 and customer beliefs 190. Customerinformation 150 is information relevant to a customer's personalcharacteristics, needs and likely use of the product.

As discussed above, customer values 180 define what is important to acustomer in the buy decision. It should be appreciated that customervalues 180 are not necessarily the same as product features. Although acustomer will make some choices with regards to specific features thathe or she desires in a product, they are ultimately influenced by theirunderlying values, which are typically more general in nature and get atwhy a customer likes a given feature (e.g., a customer may want thefeature of power windows on an automobile, but the underlying value thatmakes them purchase that feature is the convenience, or time savings).

Information about Products in Use 240 is data about the products 160being evaluated. The portion of this data that is presented to theindividual customer is based on the customer information and/or customervalues.

Customer beliefs 190 refer to the customer's understanding of a productor service and their own use of the product or service. These customerbeliefs may be based entirely or partially on the Information aboutProducts in Use 240. Customer beliefs can be changed, while customervalues generally do not change. For example, a customer may believe,based on anecdotal or direct evidence, that a product is unreliable. Thecustomer may have formed this belief on erroneous information. Thus,this customer belief may be changed by educating the customer. However,the value that the customer places on reliability is not affected by thecustomer's changing beliefs with respect to the actual reliability ofthe product.

Customer information 150, customer values 180 and customer beliefs 190are used to determine the customer value proposition 170. The customervalue proposition 170 is then used to help guide the customer regardingwhich of the products 160 to buy.

FIG. 3A is a diagram showing an example of steps that may be taken bythe buy component in assisting a customer with “self-service buying” ofcellular phone service over the internet. Each step in FIG. 3 isillustrated as a sample screen presented to the customer.

It should be appreciated that the cellular phone service exampleillustrated in FIG. 3A is just one example of the types of “self-servicebuying” transactions that the buy component 160 can be applied to. Theactual tasks performed in each step may vary greatly depending on thespecific product and type of decision to which the system is tailored.In this example, the products 160 available to the customer are digitaland analog service.

In step S130, information gathering is performed, in which the customeris asked to input information about themselves, their needs, and theirlikely use of the product. In the cellular example shown, the customeris asked to input the following information:

-   -   (1) The zip code of the area in which the customer will        primarily use the service;    -   (2) The minutes per month the customer will use the phone        service;    -   (3) Whether the phone is primarily for business or personal use;    -   (4) Where the customer travels and the percentage of their total        time they spend in these areas; and    -   (5) The maximum amount per month the customer can spend on the        service.

In step S132, information about the customer's values (180) is obtainedand assessed. In the example shown, the values the customer places oncost, quality (in terms of number of calls that get dropped), andcoverage (in terms of how often the service is available) get assessed.In order to make a meaningful comparison between the intangible valuesof “quality” and “coverage” with the tangible value of “cost”, thecustomer is preferably asked to provide information designed to assesstheir willingness to pay to get increased coverage. The following areexamples of questions that may be asked of the customer:

(1) With all service providers, occasionally calls are “dropped” and youhave to re-dial to connect with the other party. To measure howimportant continuous service is to you, consider plans A and B. UnderPlan A, an estimated 2 out of 10 calls you will make each day will bedropped. Under Plan B, an estimated 1 out of 10 calls will be dropped.Estimate how much, if any, more per month you would be willing to payfor Plan B; and(2) The areas in which your cell phone will work will vary by provider.To measure how important coverage is to you, consider plans A and B.Plan A will provide you with coverage on an estimated 8 out of 10 callsyou want to make each day. Plan B will provide you with coverage on anestimated 9 out of 10 calls. Estimate how much, if any, more per monthyou would be willing to spend for Plan B.

Although, in the example shown, the customer is queried to obtaininformation about the customer's values, it should be appreciated thatcustomer values could also be obtained by other means, such as analyzingalready existing data (e.g., already existing data on the customer'sprior purchases).

In step S134, the customer's beliefs are assessed and Information AboutProducts in Use 240 is presented to the customer. The buy componentpresents the Information About Products in Use 240 that is relevant tothe individual customer, based on the information gathered at step S130.Information About Products in Use 240 is data regarding the alternativesthat the customer is evaluating. For the cellular example shown in FIG.3, this preferably includes:

-   -   (1) The coverage (availability of service) and quality (% of        calls dropped) for various types of service;    -   (2) The typical usage pattern for customers, depending on        business or personal use (e.g., percentage of total calls made        at peak time, percentage of total calls that are long-distance,        and percentage of total calls made out of the local area); and    -   (3) The costs of services (e.g., monthly fee, long-distance fee,        roaming fees, peak-time surcharges, etc.).

The customer can accept the information presented at step S134, ormodify it with an override as a representation of their beliefs aboutthe product and the customer's own use of it. However, the customer ispreferably not allowed to modify the coverage, quality, or cost data, asthat information does not depend on personal situations, but only on theplan selected. In the sample screen shown in step S134, the customer ispresented with item (2) above, i.e., estimated usage pattern (percentageof total calls made at peak time, percentage of total calls that arelong-distance, and percentage of total calls made out of the localarea).

At step S136, the buy component 130 presents the customer with aquantitative measure and graphical representations of the customer valueproposition 170 for each cellular service alternative. For each cellularservice alternative, the following is preferably presented:

-   -   (1) Total monthly out-of-pocket costs;    -   (2) Estimated number of calls that will be dropped based on the        customer's usage pattern;    -   (3) Estimated percentage of time the customer will have coverage        available, based on the customer's usage and travel patterns;    -   (4) Total cost, including the out-of-pocket costs, and the        intangible costs (based on the customer's willingness to pay) of        having no coverage and experiencing dropped calls;    -   (5) Graphics, preferably including standard decision analysis        outputs, such as a “components of value” chart, a tornado chart        and a risk profile.

FIG. 3B shows a sample table 300 for presenting the information in item(4) above. The sample table 300 shown in FIG. 3B shows, for both analogand digital service: (1) total out-of-pocket costs; (2) estimated numberof calls that will be dropped per month; (3) the estimated coverage,expressed as a percentage; and (4) the total of the tangible andintangible costs. As discussed above, a monetary value is assigned tothe intangible costs (coverage and dropped calls) based on thecustomer's responses to the questions posed in step S132 (FIG. 3A).

FIG. 3C shows a sample “components of value” chart 310 preferablycreated at step S136 of FIG. 3A. The chart 310 shows, for both analogand digital service, a breakdown of the total out-of-pocket cost permonth. The breakdown shown in FIG. 3C includes service fees, charges foradditional minutes, long distance charges, and roaming charges. Thechart also shows, in graphical form, the total out-of pocket cost forboth analog and digital service.

FIG. 3D shows a tornado diagram 320, preferably created at step S136 ofFIG. 3A. The tornado diagram 320 shows that the percent time spentcalling inside the network and the total minutes used are the twogreatest factors driving the total monthly cost. The diagram displays abar for each variable used to calculate the total monthly cost. Thevariables used to calculate the monthly cost in the example shown are“Percent in Network” (time spent calling inside the network), “TotalMinutes Used”, “Percent Long Distance” (time spent calling longdistance), “Weekday Minutes Used”, and “Percent Peak Time” (time spentcalling during peak times).

The bars show how total monthly cost varies over a range of values ofthe variable. The range that is used is derived from probabilisticinformation that the user inputs or is obtained by the system connectingto a data source. The ends of the range usually represent the 10^(th)and 90^(th) percentiles of a probability distribution. As an example,the total monthly cost is $130 when the “Percent in Network” (time spentcalling inside the network) is low, but $340 when the “Percent inNetwork” is at its highest value. The bars are ordered based on theimpact of the range for a variable on the total monthly cost, with thevariable with the most impact on the top and the variable with the leastimpact on the bottom. For a variable that has no bars, the total monthlycost does not vary over the range of inputs for that variable.

FIG. 3E shows a risk profile graph 330, preferably created at step S136of FIG. 3A. The risk profile graph 320 shows the probabilitydistribution that represents the system's prediction of Total Cost PerMonth based on the user inputs. The predicted Total Cost per Monthranges between $60 and $485 in the example shown. The vertical axisshows the cumulative probability, or description of the likelihood ofthe cost being less than or equal to any point between $60 and $485.

Based on the information presented to the customer at step S136, thecustomer selects among the various alternatives. In the cellular serviceexample shown in FIGS. 3A–3E, analog service provides the best overallvalue proposition to the customer. Thus, the customer would selectanalog service.

The buy component 130 preferably continues to monitor the customer todetermine if the product or service chosen continues to be the bestproduct or service for maximizing customer value. This is shown as stepS138 in FIG. 3A. In the example shown in FIG. 3A, the buy component 130will contact the customer when a better service becomes available. Thebuy component 130 will preferably also contact the customer periodically(e.g., every six months) to obtain updated information from the customerabout their cellular phone usage pattern, in order to determine if theselected service continues to provide the customer with the greatestvalue.

Prior systems for self-service buying applications have black-boxrecommendation engines that either focus on the product attributes andfeatures that buyers desire or are not limited to a buyer's input. Incontrast, the buy component 130 of the present invention interacts withusers in terms of their values and needs. The buy component 130 providesusers with the information they need to understand how their values andneeds influence the choice recommended by the buy component 130. The buycomponent 130 allows users to evaluate custom alternatives around suchcriteria as pricing, packaging, promotion, quantities, shipment method,warranties, service levels, and guaranteed availability.

FIG. 4 is a schematic diagram of a preferred object-oriented analyticalmodel used by the build component 150 to help builders determine whatproducts 160 to build. The model determines a company value proposition200 based on aggregated customer values 210, company beliefs 220, andInformation about Products in use 240.

Aggregated customer values 210 preferably comprises aggregatedinformation about the values of customers that can be used to helppredict revenues for products 160 that are being considered.

Company beliefs 220 preferably comprises information regarding will costthe company to offer a product and information about potential revenues.Examples include the company's manufacturing costs, cost of capital,prediction of future market size, etc., The company beliefs may beinputs by the user or company data that may be modified by the user tofit the particular product situation.

The variables 210, 220 and 240 can be forecasts, ranges, and/orhistorical information from data sources. Furthermore, the informationis preferably gathered and represented by probability distributions toreflect the uncertainty in the information. The distribution may berepresented in its probabilistic mathematical form, by a discrete numberof points that represent the probability distribution, or presented as asingle point that represents the entire distribution.

FIG. 5A is a diagram of an example of steps taken by the build component120 in assisting companies in a “build decision” (product offerings,features, price, promotion, packaging, warranties, service levels,etc.). Each step in FIG. 5 is illustrated as a sample screen presentedto the user.

In the example shown in FIG. 5A, the decision 160 is whether to rolloutdigital service in Mountain View, Calif. In step S120 (values-basedsegmentation), demographic data for customers who would use service inMountain View is combined with the Aggregated Customer Values 210 topredict the number of people in Mountain View who will purchase digitalservice, or the market share. For example, using standard conjointanalysis, the customer value related to the number of dropped calls theyexperienced can be used to predict market share based on how many dropspeople are experiencing with a given technology. The market share isthen presented to the user with other measures of financial impact instep S124.

In step S122 (values-based product creation) the user defines potentialservices to offer, and the build component 120 calculates attributes ofvalue for each potential service. In the example shown in FIG. 5A, theattributes of value calculated are cost, drops (% of total callsdropped) and coverage provided (% of total service area covered).

In step S124 information regarding the financial impact of the potentialproducts being evaluated is determined and presented to the user. Thisfinancial impact information comprises the Company Beliefs 220, and isbased, in part, on the information provided by the user in step S122.The build component 120 preferably presents Company Beliefs 220 onrevenues, costs, and market dynamics. The user can accept the CompanyBeliefs 220, or modify them based on the specific situation of rollingout service in Mountain View.

As discussed above, the company beliefs 220, the aggregated customervalues 210, and the information about products in use 240 are used indetermining the company value proposition 200. In the example used inFIG. 5A, the company value proposition 200 is a business model thatcalculates the profitability of the product being evaluated (e.g.,digital service in Mountain View). In step S126, the company valueproposition 200 is presented, preferably as:

(1) a measure of return on investment, such as Net Present Value (NPV)over 10 years; and

(2) a set of standard decision analysis graphical outputs.

The standard decision analysis graphical outputs preferably comprise acomponents of value chart, a tornado chart, and a risk profile graph,such as the ones shown in FIGS. 5B, 5C and 5D, respectively.

The separate components of value shown in the components of value chart340 of FIG. 5B are preferably revenue, development costs, distributioncosts, operating costs and profit for each of the proposed cellularservice plans.

The tornado diagram 350 shown in FIG. 5C preferably shows how Profitvaries over a range of values of the variable. The diagram indicatesthat the range of uncertainty for Market Size and Market Share impacttotal Profit the most.

The risk profile graph 360 shown in FIG. 5D preferably shows theprobability distribution that represents the systems prediction ofProfit based on the user inputs. The predicted Profit ranges from $200Million to $1200 Million.

In step S128 of FIG. 5A (Monitoring), the rollout of the digital servicein Mountain View is monitored by the build component 120. Metrics suchas sales, customers, and costs are preferably tracked. The user candefine milestones, such as sales targets, and the build component 120preferably notifies the user when these milestones are missed or met.

Prior art systems for product offering decisions are general-purposebusiness performance management products that measure return oninvestment and that track organizational performance for corporateinvestments or activities. These prior art systems typically take a longtime to implement and modify, and use inferred customer expectations. Incontrast, the build component 150: (1) reduces implementation time byproviding packaged object-oriented analytical models; (2) improvedecision accuracy by utilizing actual vs. inferred customer values andneeds; and (4) allows companies to prioritize investments to get thebest possible corporate return.

FIG. 6 is a schematic diagram of an object-oriented analytical modelused for the sell component 140 shown in FIG. 1. As discussed above, thesell component 140, is used to help company service representatives(CSRs) and automated online service centers offer products 160 tocustomers that will improve customer value and/or company value. Themodel determines a sell proposition 270 based on the company valueproposition 200, the products 160 and the customer value proposition170.

FIG. 7 is a diagram of sample steps taken by the sell component 140 inassisting company service representatives and automated online servicecenters offer products to customers. In the example shown in FIG. 7, thealternative product offerings are analog and digital cellular service.Each step in FIG. 7 is illustrated as a sample screen presented to theuser.

In step S140, the customer value proposition 170 and the company valueproposition for the alternative product offerings of analog and digitalservice are displayed on a chart for the service representative. Theservice representative can then see that the analog service is of highervalue to the customer, whereas the digital service is of higher value tothe company.

In step S142, the service representative interacts with the customer toconduct a quick analysis to determine if a product offering will providehigher value, to either the customer, the company or both, than thechoice to go with analog service. Based on the information gathered fromthe customer, the sell component will inform the sell representativewhat promotional options he is allowed to offer to the customer.

For purposes of illustration, assume the service representativediscovers that the customer values analog service because digitalcoverage is incomplete in the customer's area, and that the servicerepresentative is informed that digital service will be made availablein the customer's area shortly. In this scenario, the sell component 140informs the service representative that he is allowed to offer threefree months of digital service to the customer while the digitalcoverage is improved in the customer's area.

In step S144, the customer chooses among the product offerings. Thethree free months of digital service enhances the customer value fordigital service. The value proposition for the customer on thisalternative is higher than that for analog service, so the customer ismore likely to choose digital service (with the three free months) overanalog service.

The sell component 140 preferably continues to monitor the customer todetermine if the product or service chosen continues to be the bestproduct or service for maximizing customer value. This is shown as stepS146 in FIG. 7. In the example shown in FIG. 7, the sell component 140will contact the customer when a better service becomes available. Thesell component 140 will preferably also contact the customerperiodically (e.g., every six months) to obtain updated information fromthe customer about their cellular phone usage pattern, in order todetermine if the selected service continues to provide the customer withthe greatest value.

Prior art systems for on-line selling applications employ a structuredprogramming approach that utilize rules-based engines. The rules-basedengines utilize a fixed set of outcomes, and take a long time toimplement. In contrast, the sell component 140 allows company CSRs andautomated online service centers to: (1) compare alternatives thatmaximize company profits alongside those that maximize individualcustomer value; (2) identify areas where further dialogue could providea choice that improves customer satisfaction; and (3) reduceimplementation and modification time due to the object-oriented modelsused.

Because object-oriented analytical models are used, all of theindividual components that make up the demand component 110, and all ofthe individual components that make up the supply component 120 can beintegrated into a closed-loop system. FIG. 8 is a schematic diagramrepresentation of the flow of information in an integrated build-to-buydecision analysis system and method, in accordance with a preferredembodiment of the present invention. FIG. 8 illustrates how the variousanalytical models discussed above can be interconnected to obtain anintegrated build-to-buy system.

As discussed above, the customer value proposition 170 is formulatedbased on customer information 150, customer values 180 and customerbeliefs 190. The company learns about the customer values 180 over timeand accumulates this knowledge about the entire market as aggregatedcustomer values 210. The aggregated customer values 210 are then used aspart of the basis for forming the company value proposition 200. Companybeliefs 220 and information about products in use 240 also form part ofthe basis for the company value proposition 200. Information on productsalready in use 240 can also be used to educate potential customers andchange customer beliefs 190.

As discussed above, the company value proposition 200 is used todetermine what products 160 to build. The customer value proposition 170is used by the customer to determine which of the products 160 to buy.

The sell proposition 270 comes into play if the company utilizes CSRs oronline service centers to help the customer make a final buy decision.If these are used, the sell proposition 270 is obtained based on thecompany value proposition 200, information on the products available forpurchase 160, and the customer value proposition 170. As discussedabove, the sell proposition 270 is one that maximizes both customer andcompany value. Using the sell proposition 270, the CSR seeks to improveboth company and customer value.

As discussed above, the object-oriented analytical models describedabove are preferably implemented using an object-oriented programminglanguage, such as Java™, C⁺⁺, and Smalltalk. The present invention ispreferably implemented as an Internet-based system and method. Thus, theVarious components of the present invention are preferably implementedon a server, which may be or include, for instance, a work stationrunning the Microsoft Windows™ NT™, Windows™ 2000, UNIX, LINUX, XENIX,IBM, AIX, Hewlett-Packard UX™, Novel™, Sun Micro Systems Solaris™,OS/2™, BeOS™, Mach, Apache Open Step™, Apple OS X™ or other operatingsystem or platform. However, the various components of the presentinvention could also be implemented on a programmed general purposecomputer, a special purpose computer, a programmed microprocessor ormicrocontroller and peripheral integrated circuit elements, an ASIC orother integrated circuit, a hardwired electronic or logic circuit suchas a discrete element circuit, a programmable logic device such as aFPGA, PLD, PLA, or PAL, or the like. In general, any device on which afinite state machine capable of implementing the object-orientedanalytical models illustrated in FIGS. 1–8 can be used to implement thepresent invention.

While this invention has been described in conjunction with the specificembodiments outlined above, it is evident that many alternatives,modifications and variations will be apparent to those skilled in theart. Accordingly, the preferred embodiments of the invention as setforth above are intended to be illustrative, not limiting. Variouschanges may be made without departing from the spirit and scope of theinvention as defined in the following claims.

1. A computer-implemented decision analysis system that helps users makevalues-based decisions by leading them through a set of tools and aprocess to clarify and quantify their values, search for and identifythe most attractive alternatives given their values, search for andidentify the most relevant information given both their values andalternatives, and integrate all of these elements to reach testable,documented conclusions, and facilitates decision making by integrating avalues-based demand component for buyers of a product or service and avalues-based supply component for sellers with feedback loops betweenthe components and value optimization algorithms that enable the systemto identify beneficial commercial transactions for participatingparties, the system comprising: means for creating a demand componentcomprising at least one demand-oriented values-based decision analysiscomponent, the demand component being based on: i) a set of product orservice characteristics and qualities; ii) a set of specific buyerdemand values that quantitatively measure a buyer's relative preferencefor each product or service characteristic and quality; iii) a set ofgeneral buyer demand values, predetermined by the buyer independent ofthe product or service characteristics and qualities, thatquantitatively measure a buyer's preferences for making decisions,including risk preference, time value of money, preferred way to processinformation, and personality type; iv) a set of buyer value tradeoffsshowing how the buyer would trade on demand value for another demandvalue and showing how all demand values mathematically relate to onecommon financial metric; v) a set of buyer-specific informationcomponents, representing a buyer's unique beliefs about a product orservice, codified in terms of a probability or a probabilitydistribution; and vi) a set of general information demand componentsgathered from across all similar values-based demand components,prioritized by similarity of the buyer's specific values with the valuesof other buyers, creating values-based demand components; vii) a set ofgeneral information supply components gathered from across allvalues-based supply components created by sellers, prioritized by thebuyer's specific values and the similarity of the products and servicesthe buyer has identified as alternatives with the products and servicesthe seller is selling, viii) a set of buyer alternatives that representat least one of products and services the buyer is capable ofpurchasing; means for creating a supply component comprising at leastone supply-oriented, values-based decision analysis component, thesupply component being based on: i) the set of product or servicecharacteristics and qualities; ii) a set of specific seller supplyvalues that quantitatively measure a seller's relative preference foreach product or service characteristic and quality, iii) a set ofgeneral seller supply values, predetermined by the seller independent ofthe product or service characteristics and qualities, thatquantitatively measure a seller's preferences for making decisionsincluding risk preference, time value of money, preferred way to processinformation, and personality type; iv) a set of seller value tradeoffsshowing how the seller would trade one supply value for another supplyvalue and showing how supply values mathematically relate to one commonfinancial metric; v) a set of seller-specific information components,representing a seller's unique beliefs about a product or service,codified in terms of a probability or a probability distribution; andvi) a set of general information supply components gathered from acrossall similar values-based supply components, prioritized by similarity ofthe seller's specific values with the values of other sellers, creatingvalues-based supply components; vii) a set of general information demandcomponents gathered from across all values-based demand componentscreated by buyers, prioritized by the seller's specific values with thevalues and the similarity of the products and services the seller isoffering with the products and services the buyer is consideringalternatives; viii) a set of seller alternatives that represent at leastone of products and services the seller is capable of selling; means forcombining the specific and general buyer demand values, the specific andgeneral seller supply values, the seller supply values, the demand valuetradeoffs, the supply value tradeoffs, the buyer information componentsand the seller information components to evaluate risk and returncharacteristics of the buyer alternatives and the seller alternatives;and means for performing sensitivity analysis to show how the risk andreturn characteristics of the buyer alternatives and the selleralternatives change as the demand values, the supply values, the demandvalue tradeoffs, the supply value tradeoffs, the buyer informationcomponents and the seller information components change.
 2. The systemof claim 1, wherein the at least one demand-oriented, values-baseddecision analysis component, and the at least one supply-oriented,values-based decision analysis component each comprises at least oneobject-oriented analytical model that shows both graphically andmathematically how the demand values, the supply values, the buyerinformation components and the seller information components are relatedin order to calculate the demand value tradeoffs, the supply valuetradeoffs, risk and return.
 3. The system of claim 2, wherein eachobject-oriented analytical model collects user information whileassisting users in a commercial decision making process and is able toshare the user information with other object-oriented analytical models,wherein user information collected includes at least value assessments,value tradeoffs, probability assessments, alternatives considered, andalternatives selected.
 4. The system of claim 1, wherein the at leastone demand-oriented, values-based decision analysis component comprisesa buy component for assisting a customer in choosing a product among aplurality of products.
 5. The system of claim 4, wherein the buycomponent comprises an object-oriented analytical model.
 6. The systemof claim 1, wherein the at least one supply-oriented, value-baseddecision analysis component comprises a sell component for assisting auser in determining how to offer already built products to customers. 7.The system of claim 6, wherein the sell component comprises anobject-oriented analytical model.
 8. The system of claim 1, wherein theat least one supply-oriented, values-based decision analysis componentcomprises a build component for assisting a user in determining productsto build.
 9. The system of claim 8, wherein the build componentcomprises an object-oriented analytical model.
 10. The system of claim1, wherein the feedback loops are created so that demand informationgathered by the demand component in the process of helping customersmake purchase decisions is utilized by the supply component, and supplyinformation gathered by the supply component in the process of helpingproviders make, or build products or services, or offer decisions isutilized by the demand component.
 11. The system of claim 1, wherein theat lest one demand-oriented, values-based decision analysis componentcomprises a buy component and a sell component.